Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Enhancing Statistical Language Modelling and Lexical Analysis Using Sanskrit’s Linguistic Framework

Authors
Namrata Tapaswi1, *
1Department of CSE (AIML), Acropolis Institute of Technology and Research, Indore, MP, India
*Corresponding author. Email: namratastapaswi@gmail.com
Corresponding Author
Namrata Tapaswi
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_78How to use a DOI?
Keywords
Lexical; Syntactic; Semantic; Government and Binding (GB); Lexical Functional Grammar (LFG); Morphology; Context Free Grammar (CFG)
Abstract

Many Indian languages can trace their ancestry back to Sanskrit, the oldest language known to man. It is essential to represent information in a native language since the linguistically divided population is adjusting to new technologies. This has prompted several forms of study in the field of natural languages aimed at improving the process of translating spoken or written languages into English. It is easier to map translation procedures for other dialects or language understanding algorithms when Sanskrit is inherited with the linguistic hierarchy of grammar formulation. The extensive vocabulary of Sanskrit makes its grammatical validations and lexical analyses reliable research. Sanskrit grammar made it easy to evaluate vernacular languages from a semantic perspective by analyzing their morphology and lexicon. The current approaches to statistical language modeling using Sanskrit are summarized in this article. This paper lays out the entire procedure for extracting expressions and rationally deducing phrase grammar rules. Statistical modelling theories are the main subject of the article, which also offers suggestions for improving the grammar’s precision.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_78How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Namrata Tapaswi
PY  - 2025
DA  - 2025/05/26
TI  - Enhancing Statistical Language Modelling and Lexical Analysis Using Sanskrit’s Linguistic Framework
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 1045
EP  - 1058
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_78
DO  - 10.2991/978-94-6463-716-8_78
ID  - Tapaswi2025
ER  -